26 citations · 67 across the 11 of their papers we have counts for
5 papers · 1 filter
Accurate Distances Measures and Machine Learning of the Texture-Property Relation for Crystallographic Textures Represented by One-Point Statistics
Tarek Iraki, Lukas Morand, Norbert Link +2
The crystallographic texture of metallic materials is a key microstructural feature that is responsible for the anisotropic behavior, e.g., important in forming operations. In mate…
Machine learning for structure-guided materials and process design
Lukas Morand, Tarek Iraki, Johannes Dornheim +3
In recent years, there has been a growing interest in accelerated materials innovation in the context of the process-structure-property chain. In this regard, it is essential to ta…
Neural Networks for Constitutive Modeling -- From Universal Function Approximators to Advanced Models and the Integration of Physics
Johannes Dornheim, Lukas Morand, Hemanth Janarthanam Nallani +1
Analyzing and modeling the constitutive behavior of materials is a core area in materials sciences and a prerequisite for conducting numerical simulations in which the material beh…
Machine learning-based sampling of virtual experiments within the full stress state
Alexander Wessel, Lukas Morand, Alexander Butz +2
This paper presents a new machine learning-based approach to investigate anisotropic yield surfaces of sheet metals by means of virtual experiments. The new sampling approach is ba…
A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization
Tarek Iraki, Lukas Morand, Johannes Dornheim +2
The optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed t…